top k selection

ranking frequency

max heap simulation

greedy selection

two heaps

merge k sorted structures

graph + heap

sliding window + heap

sorting + heap

interval + heap

1. Size-K Heap

• Keep only k elements → O(n log k)

3. Heap + Map Combo

• count → push into heap

5. Heap + Greedy

• always take best next option

2. Min vs Max Heap

• Python = min heap
• simulate max with negatives

4. Two Heaps

• balance halves for median

use heap when you see:

• You need repeated access to the best element
• You cannot afford full sorting
• You are processing dynamic or streaming data
• “top k” / “kth largest”
• “most frequent”
• “smallest/largest repeatedly”
• “stream of data”
• “merge sorted lists”